Big Data Recommender Systems: Application Paradigms (Computing and Networks)
by: Osman Khalid
ISBN-10: 1785619772
ISBN-13: 9781785619779
Released: 2019-08-29
Pages: 520 pages
Book Description
First designed to generate personalized recommendations to users in the 90s,recommender systems apply knowledge discovery techniques to users’ data to suggest information,products,and services that best match their preferences. In recent decades,we have seen an exponential increase in the volumes of data,which has introduced many new challenges.
Divided into two volumes,this comprehensive set covers recent advances,challenges,novel solutions,and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations,algorithms and architectures,approaches for big data,and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 23 chapters.
Contents
Foreword
1Introduction to big data recommender systems-volume 2
2Deep neural networks meet recommender systems
3 Cold-start solutions for recommendation systems
4 Performance metrics for traditional and context-aware big data recommender systems
5 Mining urban lifestyles: urban computing,human behavior and recommender systems
6Embedding principal component analysis inference in expert sensors for big data applications
7 Decision support system to detect hidden pathologies of stroke: the CIPHER project
8 Big data analytics for smart grids
9 Internet of Things and big data recommender systems to support Smart Grid
10 Recommendation techniques and their applications to the delivery of an online bibliotherapy
11 Stream processing in Big Data for e-health care
12 How Hadoop and Spark benchmarking algorithms can improve remote health monitoring and data management platforms?
13Extracting and understanding user sentiments for big data analytics in big business brands
14 A recommendation system for allocating video resources in multiple partitions
15 A mood-sensitive recommendation system in social sensing
16 The paradox of opinion leadership and recommendation culture in Chinese online movie reviews
17 Real-time optimal route recommendations using MapReduce
18 Investigation of relationships between high-level user contexts and mobile application usage
19 Machine learning and stock recommendation
20 The role of smartphone in recommender systems: opportunities and challenges
21Graph-based recommendations: from data representation to feature extraction and application
22 AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs)based domain name detection systems and
application of deep learning
Index
Back Cover
Big Data Recommender Systems – Volume 2 9781785619779.pdf[/erphpdown]
